2 research outputs found
A Geometric Approach to Joint 2D Region-Based Segmentation and 3D Pose Estimation Using a 3D Shape Prior
©2010 Society for Industrial and Applied Mathematics. Permalink: http://dx.doi.org/10.1137/080741653DOI: 10.1137/080741653In this work, we present an approach to jointly segment a rigid object in a two-dimensional (2D) image
and estimate its three-dimensional (3D) pose, using the knowledge of a 3D model. We naturally
couple the two processes together into a shape optimization problem and minimize a unique energy
functional through a variational approach. Our methodology differs from the standard monocular
3D pose estimation algorithms since it does not rely on local image features. Instead, we use global
image statistics to drive the pose estimation process. This confers a satisfying level of robustness
to noise and initialization for our algorithm and bypasses the need to establish correspondences
between image and object features. Moreover, our methodology possesses the typical qualities of
region-based active contour techniques with shape priors, such as robustness to occlusions or missing
information, without the need to evolve an infinite dimensional curve. Another novelty of the
proposed contribution is to use a unique 3D model surface of the object, instead of learning a large
collection of 2D shapes to accommodate the diverse aspects that a 3D object can take when imaged
by a camera. Experimental results on both synthetic and real images are provided, which highlight
the robust performance of the technique in challenging tracking and segmentation applications